Theory Drift in Economic Voting Models of Public Opinion: Perhaps the Economy Doesn’t
Always Matter
Raymond M. Duch
University of Houston
Houston, Texas 77204-3474
[email protected]
Harvey D. Palmer
University of Mississippi
[email protected]
Authors’ note: This research was generously supported by Duch’s NSF Grant # SBR 4600 306.
1
Abstract
We contend that the application of economic voting theory to explain public support for
political institutions represents an example of “theory shift” where scholars have presumed that a
proven theory of incumbent support also has relevance in “peripheral” contexts. Yet, traditional
specifications of peripheral economic voting models to explain democratic satisfaction and
public support for European Union perform remarkably well despite their weak theoretical
foundations. This poses an empirical puzzle. We demonstrate that the empirical puzzle is a
product of poor model specification and systematic measurement error. More specifically, we
contend that the systematic measurement error in national economic evaluations – economic
assessments that are unrelated to the economy as a policy outcome – produce a spurious
relationship between economic evaluations and support for political institutions due to the direct
effect of this systematic measurement error on attitudes toward these institutions. We present a
method for estimating economic voting models that accounts (controls) for measurement error.
The empirical analysis is based on 1984 Eurobarometer (Study 21) survey data from France,
West Germany, Italy and the United Kingdom. We demonstrate that in the case of the
incumbent vote models (i.e., the conventional economic voting models), there is no change in the
estimated effect of national economic evaluations on vote choice when controlling for
measurement error in national economic evaluations. In the case of the “peripheral” economic
voting models—democratic satisfaction and EU support—we find that controlling for
measurement error in economic evaluations significantly weakens, if not eliminates, the evidence
of a relationship with economic evaluations.
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Introduction
Economic voting is a firmly established and widely recognized model in political science
(Lewis-Beck and Stegmaier 2006; Duch and Stevenson 2006). Reflecting this status, studies of
public opinion and political behavior have applied economic voting hypotheses in a wide variety
of substantive contexts. In some instances, scholars seem to assume economic voting by default
without providing well-reasoned theoretical arguments for why it should have relevance in the
specific context being analyzed. This application of economic voting by association rather than
reason represents “theory drift” in that broad acceptance of a theory leads scholars to presume its
applicability in a wide variety of “peripheral” contexts. Research on public support for political
institutions provides a case in point.
In its original formulation, the economic voting model assumes that voters evaluate the
current government in terms of the outcomes produced by its policies, with the economy being a
policy outcome of particular interest. Essentially, economic voting is a type of policy voting
because, as Kramer (1983) points out, voters are responding to the politically relevant portion of
macro-economic outcomes (see also the discussions of Erikson 2004 and Hibbs 2006). This
formulation is reflected in Kramer’s (1971) seminal study as well as in Stigler’s (1973) critique
of it. Note that Stigler debated the logic of Kramer’s theory on the grounds that the U.S. House
of Representatives exerts greater influence on the distribution of wealth than over
macroeconomic policies and hence should not be held accountable for the economy. Clearly,
both Kramer and Stigler presumed that voters hold governments responsible for their policies
rather than simply reacting in an emotional fashion to shifts in the economy.
Some recent studies have applied the economic voting model to explain cross-national
variation in democratic satisfaction (e.g., Karp, Banducci and Bowler 2003; Anderson and
Guillory 1997; Weatherford 1984, 1987) and public support for the European Union (e.g.,
Hooghe and Marks 2005; Eichenberg and Dalton 1993; Gabel and Whitten 1996; Anderson and
Kaltenthaler 1996). We believe that these studies represent examples of “theory drift” since they
adopt “peripheral” economic voting models. When modeling democratic satisfaction, we
seriously question the logic of expecting short-run policy outcomes to influence support for
political institutions, especially in mature democracies. In the case of European Union (EU)
support, we question why voters should be expected to hold the EU responsible for domestic
3
economic outcomes (especially during the 1970s and 1980s) when its policies affect those
outcomes only indirectly and much more modestly than domestic politics and the world
economy.
The central puzzle though is not so much the rationale of assertions associated with
“theory drift” but the existence of empirical evidence that appears to confirm these assertions. In
the context of “peripheral” economic voting models, the aforementioned studies have found that
economic evaluations have significant effects on democratic satisfaction and EU support. Why
would such evidence emerge if the underlying theory is suspect? The empirical validation is
particularly surprising given that these studies have relied on evaluations of the general economy
that represent rather blunt measures of policy-related economic performance. This essay
attempts to address this empirical puzzle.
Building upon Palmer’s (1999) analysis of pocketbook economic voting, we posit that
evidence supporting peripheral economic voting models might be an artifact of poor model
specification and measurement error associated with assessments of the national economy.
While theory has posited the existence of a pocketbook relationship at the individual level,
empirical evidence has largely failed to confirm it. Palmer (1999) finds though that proper
modeling of the systematic measurement error in evaluations of personal financial situation
strengthens the evidence of pocketbook economic voting in U.S. presidential elections. More
generally, Palmer’s analysis demonstrates that the failure to properly model measurement error
can confound empirical tests of a well-reasoned, convincing theoretical argument.
In the present context, we adapt the logic of Palmer’s (1999) approach in order to make
the opposite argument: the failure to properly model measurement error strengthens the evidence
that the economy influences democratic satisfaction and EU support. We contend that
evaluations of the national economy include measurement error as well as policy-related
concerns. Furthermore, we believe that the measurement error is systematic rather than random
(see Duch, Palmer and Anderson 2000) and stems from distinct sources that are independently
correlated with support for political institutions (e.g., government partisanship). Hence, the
nature of the economic voting relationship is distorted in models that do not account for
measurement error in economic evaluations. More specifically, evidence of “peripheral”
economic voting relationships is potentially spurious due to the failure to control for the distinct
4
sources of systematic measurement error. At the very least, models that “decompose” economic
evaluations into their policy-related and measurement error components will produce more
precise estimates of the relationship between the economy and public support for political
institutions.
The remainder of this essay is organized into four sections. We begin with a discussion
of “theory drift” and how it applies to “peripheral” economic voting models of democratic
satisfaction and EU support. The next section discusses the nature of heterogeneity in national
economic evaluations, giving particular attention to the distinct sources of systematic
measurement error. Then we present a method for estimating economic voting models that
accounts (controls) for measurement error. The third section applies this method to estimate
economic voting models of incumbent vote, democratic satisfaction, and EU support. Separate
models are estimated for France, West Germany, Italy and the United Kingdom using the 1984
Eurobarometer (Study 21) data. The incumbent vote analysis demonstrates that the evidence of
economic voting improves when the model controls for systematic measurement error and
thereby generates an estimate of the policy relationship that is “cleaner” from both a theoretical
and empirical perspective. We also show though that the opposite holds for democratic
satisfaction and EU support. This finding suggests that these “peripheral” economic voting
relationships are largely spurious and attributable to measurement error rather than policy
concerns (as theorized). Finally, we conclude with a section discussing the broader implications
of our theoretical argument and empirical findings for economic voting research.
“Theory Drift” and “Peripheral” Economic Voting Models
What we label “theory drift” represents the application of theory to a substantive context
that is only loosely related (or peripheral) to that for which it was developed. We include models
in this classification that employ national economic evaluations to explain phenomena that are
only tangentially linked to government economic policy. Moreover, we argue that this
theoretical weakness characterizes efforts to explain support for political institutions, such as
support for democratic institutions and for the EU.
The strength of a theoretical argument rests on its ability to clearly and logically connect
the outcome behavior to the motivations of the political actors. For this reason, it is necessary to
5
be quite clear as to what the economic voting theory states. Recall that in the sanction model,
economic voters are confronted with a moral hazard problem when deciding on voting for the
incumbent versus opposition parties (Barro 1973; Ferejohn 1986; Hibbs 2006). If voters do not
sanction economic performance, they risk signaling to incumbents that poor economic
performance would be tolerated and hence invite rent seeking on the part of self-interested
political candidates. In this model, voters are not engaging in the comparative assessment of
utility income streams from competing political candidates or outcomes– rather they simply
establish a threshold performance level and re-elect incumbents that satisfy this requirement and
punish those that do not (Ferejohn 1986). This leads to the sanctioning feature that characterizes
most accounts of the economic vote. It is concern about future re-election prospects that
motivates incumbents to avoid shirking their responsibilities. They anticipate that voters will
sanction them if they under-perform. And in order to maintain the credibility of this threat,
voters punish incumbents at the polls when retrospective economic performance is substandard.
And as we noted above, voters are expected to respond to only the politically relevant portion of
macro-economic outcomes.
Rather than preferences over political candidates, the dependent variable in theory drift
models is preferences over political institutions. Political institutions, as part of a larger political
system, constitute the process by which government policies are formulated. The strength of
public support for a country’s political institutions reflects the extent of legitimacy for its
political system. Is it reasonable to think of the electorate as sanctioning political institutions by
withholding this legitimacy in response to economic outcomes?
A plausible foundation for these arguments – although one that clearly does not conform
to the moral hazard features of the sanctioning model of economic voting – is the notion that
popular legitimacy is tied to the outputs of political institutions. The classic statement of this
relationship is Easton’s notion that citizen satisfaction with government outputs can build diffuse
support for a political system (Easton 1965). Unsatisfied demands or poor outputs could
compromise public support for the institutions. The “theory drift” literature addressed here
assumes that popular support for political institutions – or preferences over different types of
institutional arrangements – will respond to (short-run) fluctuations in macro-economic
outcomes.
6
There is evidence that economic performance can play an important role in determining
the success or failure of nascent political institutions in the case of nations at the formative
periods of the institution-building process (Lipset 1959; Londregan and Poole 1990, 1996;
Haggard and Kaufman 1995). The inference frequently made in the literature is that these
successes or failures result because of the impact that economic performance has on public
support for political institutions (Pye 1971). But evidence regarding the link between the
economy and support for political institutions is even ambiguous in these transition contexts.
Duch (1993, 1995) has presented evidence suggesting that even at the formative stages of
democratic consolidation (specifically in post-Soviet countries for his empirical analyses) public
support for democratic institutions is at best weakly related to economic outcomes. In fact, Duch
(1995) finds that for the most part sanctioning resulting from perceived economic performance
tends to be confined to incumbent governments.
While political legitimacy may be responsive to policy outcomes in contexts with fragile
or nascent political institutions, mature democracies generally benefit from a reservoir of support
for their political institutions, which causes voters to blame particular governments rather than
the political system for poor policy outcomes.1 And while there clearly is considerable debate as
to how best to measure popular feelings of legitimacy towards political institutions, there is a
substantial body of empirical work suggesting that in fact the public in mature democracies have
quite stable attitudes supporting democratic institutions (Sniderman et al 1975; Sniderman 1981;
Weatherford 1984).2 In other words, voters respond to negative outcomes by expressing their
dissatisfaction with those actors that have gained predominance in the policy-making process
rather than with the process itself.
The theoretical confusion here is that the “theory drift” literature has employed the
sanctioning model of economic voting to explain preferences in a fashion that simply does not
meet the central premise of the model. To illustrate, consider a typical economic model of
support for democratic institutions specified as follows:
(1.1)
1
Yi = β 0 + β1X i + φ1Z i
An example is of this is what Persson and Tabellini (2005) label as a reservoir of “democratic capital”.
Much of this controversy has concerned the measurement of political trust (Citrin 1974; Abramson and Finifter
1981).
7
2
In this notation, Y indicates support for democratic institutions by individual i. X i are national
economic evaluations (NEE) measured at the individual level and Z i are other characteristics of
individuals that shape attitudes toward democratic institutions. The national economic
evaluations in these models are typically the retrospective socio-tropic variety – respondents’
assessment of changes in national economic conditions over the past 12 months. Hence, the
logic of these arguments is that changes in assessments of short term policy outcomes ( X i ) will
have significant effects on attitudes toward democratic institutions – voters “sanctioning” these
institutions.
This strikes us as implausible. First, the relationship between policy and legitimacy in
mature democracies only emerges after persistent trends in poor outcomes demonstrate a failure
in the policy-making process (Weatherford 1987). We should expect cross-national variation in
political legitimacy to emerge only as a result of persistent national differences in government
performance (e.g., Almond and Verba 1963; Powell 1982; Almond and Powell 1988). Hence,
we take issue with models that purport to establish an ongoing relationship between short-term
fluctuations in economic performance or economic evaluations (which are typically measured
over a 12 month period) and attitudes toward mature political institutions. We cannot conceive
of any convincing theoretical reason to expect a persistent policy relationship to exist well
beyond the early period of institution building. While policy concerns might gain relevance
periodically, perhaps during periods of political crisis, the notion of an ongoing causal
relationship seems fundamentally inconsistent with the stabilizing role of political institutions.
A second and related concern is that this makes little sense given the moral hazard or
sanctioning theoretical foundation of economic voting models. These models, even in their
“sociotropic” versions, essentially posit that self-interested citizens respond to the policy
initiatives (and outcomes) of political decision-makers by punishing or rewarding those
responsible. But institutions do not make policy decisions. These actions are the purview of
elected politicians, government administrators, and other political actors with visible roles in the
policy-making process. Moreover institutions are not rent seeking actors that create moral
hazard incentives for citizens.
A more plausible interpretation of these peripheral economic voting models is to interpret
8
them as a simple distortion of the underlying rational policy relationship that is based on a broad
emotional response by voters to short-run shifts in the economy. Voters do not try to induce
good behavior when they respond to the economy; rather they simply get mad, disappointed, or
annoyed when the economy is performing poorly and this gets reflected in a broad range of
political attitudes and behavior (Lodge, McGraw and Stroh 1989; Clarke, Stewart, and Whiteley
1998; Zaller 1992; Marcus, Neuman, and MacKuen 2000). This emotional response implies that
voters either hold everyone and everything accountable or simply shift their political attitudes to
reflect their economic mood. Either explanation is clearly inconsistent with the rational policyoriented core of the economic voting argument.
We now explore two examples of peripheral economic voting models of political
institutions: democratic satisfaction and support for the European Union.
Democratic Satisfaction. It is widely accepted that the proper functioning of
democratic institutions is somehow linked to public attitudes toward these institutions. A
citizenry that is discontented with how democratic institutions perform or function will
undermine the functioning of these institutions or possibly represent a threat to their longevity.
Accordingly a considerable literature has developed over the past two decades devoted to
explaining why levels of democratic satisfaction vary. And there has been a proliferation of
studies purporting to demonstrate that attitudes toward democratic institutions or satisfaction
with democratic institutions are linked to economic performance.3 These “economic models” of
support for democracy have been particularly popular given the simultaneous transition of postcommunist nations to democracy and market economies. The significant economic dislocation
that occurred at the same time these countries adopted nascent democratic institutions was
generally considered a serious threat to support for democracy (see Mishler and Rose 1996,
although see Duch 1995). But the hypothesized relationship has not been confined to transition
democracies. For example, Anderson and Guillory (1997) argue that in the wake of the recent
fall of the Iron Curtain and poor economic performance in Western Europe, European support
for democracy has declined somewhat. Clarke, Dutt, and Kornberg (1993) and Karp, Banducci
3
Again, its important to recognize that the economic evaluation variables in these peripheral
models are short-term economic evaluations that typically are based on questions referring to the
performance of the overall national economy over the past 12 months (or the next 12 months in
9
and Bowler (2003) make similar claims regarding perceptions of economic performance and
levels of democratic satisfaction. Typical of claims regarding the correlation between the
economy and support for democratic institutions is the argument made by Anderson and Guillory
(1997, 67): “Furthermore, economic difficulties…have magnified the loss of enthusiasm for
democratic politics among people in Western Europe because the shortcomings of democratic
governance have been put in sharper relief than previously.”
We find it implausible that the average citizen would lose enthusiasm for democratic
institutions simply because of a downturn in the economy – or would “punish” democratic
institutions to use the sanctioning terminology. Nevertheless, many of these scholars report
individual-level equations of “democratic support” or “democratic satisfaction” with significant
positive correlations between NEE and support for democracy.4
Support for European Integration. The link between economics and political attitudes
has played a central role in the history of European unification over the past five decades. Part
of the mythology of European unification is the notion that narrow economic agreements, with
positive economic pay-offs for the average citizen, would expand and eventually evolve into a
much broader political union. A number of students of European unification have proposed an
individual level interpretation of these dynamics. European citizens, they argue, condition their
support for further efforts at European unification in terms of their evaluations of current or
future economic performance.
The link between macroeconomic outcomes at the national level and support for the
European Union (EU) has been made on a number of fronts. On the one hand, there is a wealth
of impressionistic accounts arguing that the European mass public is more receptive to the EU,
presumably both political and economic, during favorable economic times. But when economic
performance falters, it seems that public support for the European Union also declines. For
example, during the early 1990s which was a period of weak economic performance in Europe,
the case of prospective economic evaluations).
4
Some have challenged this argument on the grounds that democratic satisfaction is actually measuring evaluation
of incumbent performance. While others have provided convincing evidence regarding the construct validity of this
measure (Clarke and Kornberg 1992). We are agnostic on the appropriateness of this measure. Our reservations
concern the plausibility of the argument that short-term fluctuations in the economy shape attitudes toward
democratic institutions. Our criticism of the literature concerns the inclusion of economic evaluations in these
equations that are plagued by measurement error which we contend inflates the hypothesized correlations.
10
particularly with respect to job creation, support for European unification declined, from a high
of 81% in 1991 to 73% in the spring of 1994 (1996). More rigorous efforts at establishing this
link have also been proposed. One approach builds on the economic voting literature, arguing
that fluctuations in national macro-economic conditions will affect EU support. This approach
was originally adopted by Eichenberg and Dalton (1993) but has been applied more recently by
other students of European integration (e.g., Gabel and Whitten 1996; Anderson and
Kaltenthaler 1996; Hooghe and Marks 2005). And some have even argued that domestic
economic outcomes have shaped the support for the EU in prospective member countries
(Christin 2005).
The link between short-term policy outcomes and public support for the EU strikes us as
more plausible than their association with public support for more mature democratic political
institutions. EU institutions might not benefit from broad legitimacy and hence the European
publics may in fact blame EU institutions rather than policy-makers for short-run policy failures.
Nevertheless, there is only a weak connection between EU policy and domestic economic
performance. Until recently, the EU was responsible for neither fiscal nor monetary policies
(and this certainly was the case for the period covered by our survey data testing this argument).
And while EU membership represents a constraint on national economic policies, historically,
this effect has been indirect and hence we seriously doubt that the average citizen has the
sophistication and knowledge to recognize this linkage.
In sum, our general criticism of peripheral economic models of EU support is that until
recently the policy initiatives of the EU have had very little effect on economic outcomes.
Arguably, domestic politics and the global economy shape a country’s economic performance
much more than EU policy does. Hence, the theoretical justification for a traditional economic
voting model of EU support is weak at best.
Speculations on the Appropriate Specification of “Peripheral” Economic Voting Models
Our discussion of the peripheral economic voting models of democratic satisfaction and
EU support lead us back to the empirical puzzle that motivates this essay. If the theory is
suspect, then how do we account for the empirical evidence of correlation between national
11
economic evaluations and public support for political institutions? A critical assumption in
peripheral economic voting models is that the economic evaluation variables on the right-hand
side of these equations are free of measurement error that might bias the results. As we (Duch,
Palmer and Anderson 2000) and others (Kramer 1981; Erikson 2004; Evans and Andersen 2006;
Hibbs 2006) have demonstrated, this is a questionable assumption. What is this measurement
error and why should we expect it to be particularly troublesome in the case of models derived
from “theory drift”? Much of the insights into the nature of this measurement error have been
developed in the study of U.S. economic voting models.
Error in the measurement of personal financial situation (PFS) is frequently identified as
contributing to the weak relationship between pocketbook concerns and vote choice or
evaluations of incumbents. Markus (1988) characterized the measurement error as essentially
random – any systematic component of PFS is not considered measurement error even if it
captures highly personal factors unrelated to government policy making. Kramer (1983), on the
other hand, suggested that the measurement error in PFS is systematic, incorporating factors that
are unrelated to government-induced economic changes – such as partisanship. By adopting
Kramer’s specification of the measurement error, Palmer (1999) has shown that there is in fact a
relationship between pocketbook assessments and reported vote in presidential and congressional
elections. In controlling for the systematic factors that shape PFS evaluations but are unrelated
to government-induced policy outcomes (e.g., partisanship and life-cycle circumstances),
Palmer’s equations essentially estimated a “clean” pocketbook effect, i.e., one that is directly
attributable to government-induced policy outcomes.
In this essay, we posit that NEE suffer from the same sources of systematic measurement
error that plague PFS evaluations. This suggests that our measure of NEE incorporates
measurement error,
(1.2)
X i = X iO + X i S + ε i
XiS = f ( W )
where, X i O is the objective economic evaluation, X i S captures systematic differences due to
information and subjective factors (i.e., W), and ε i is the stochastic component. In this formal
definition, individual-level evaluations contain two forms of “noise”: subjective considerations
12
and random fluctuations. Both forms of noise constitute sources of non-attitudes. In previous
research (Duch and Palmer 2002), we modeled X S , leaving the objective evaluation and the
stochastic component in the disturbance.
But we contend that the systematic measurement error in NEE affects estimation of the
economic voting relationship in exactly the opposite manner as for PFS evaluations. The
systematic factors contributing to NEE measurement error could in fact artificially inflate the
correlation between NEE and the dependent variable. An example developed elsewhere (Duch,
Palmer and Anderson 2000) considers the case of economic voting in U.S. presidential elections
where NEE is strongly shaped by partisan pre-dispositions. If we include NEE in an economic
voting model without controlling for this systematic measurement error ( X i S ) , NEE will “pick
up” the direct effect of partisan pre-dispositions, thereby producing an inflated estimate of the
relationship between NEE and vote choice.
We expect this problem to be exaggerated in models derived from “theory drift”. Models
derived from “theory drift” have dependent variables that we contend are only tangentially
linked to economic policy outcomes. Nevertheless, the NEE variables exhibit a high correlation
with the dependent variable primarily because of the systematic component of measurement
error in NEE ( X i S ) . In the conventional economic voting model, systematic measurement error
in NEE is defined as factors that shape NEE but are essentially unrelated specifically to
economic policy outcomes – in other words the distinction between X i S and X i O . We expect that
much of the systematic measurement error that shapes NEE but is not specifically related to
economic policy outcomes ( X i S ) is highly correlated with dependent variables in “theory drift”
models. The reason is simply that the dependent variables in most of these “theory drift” models
tend to be rather diffuse political evaluations that have the same psychological antecedents as
economic evaluations – the Z control variables in equation (1.1) are very similar to the W
variables predicting X i S in equation (1.2). For example, there is considerable evidence
suggesting that economic evaluations are shaped by partisanship. Because partisanship tends
also to be strongly correlated with institutional evaluations (a typical dependent variable in these
theory drift models), not addressing the measurement error problem results in an inflated
13
correlation between economic evaluations and the dependent variable. We now explore this
problem with respect to democratic satisfaction and support for European unification.
Systematic measurement error as a source of augmentation bias. The argument that
systematic measurement error inflates the correlation between economic evaluations and support
for political institutions is based on two sets of expectations. First, the economic evaluation
measures in these models typically are not exogenous. Second, some of the systematic error
associated with the measurement of economic evaluations is also correlated with democratic
satisfaction and EU support. Our model of variation in economic evaluations can be broken into
the three categories identified in equation (1.2): 1) random (i.e., unsystematic) measurement
error ( ε i ) ; 2) systematic measurement error ( X i S ) ; and 3) factors contributing to meaningful
fluctuations in economic evaluations ( X i O ) . By controlling for systematic and random
measurement error in economic evaluations, we expect to be able to better evaluate the actual
link between economic perceptions and public support for political institutions.
Essentially, systematic measurement error makes NEE subjective rather than purely
objective. While purely objective NEE may vary across individuals, this variation is
“meaningful” with respect to the relationship between the economy as a policy outcome and the
political actor/institution held responsible for that outcome. In other words, variation in the
objective, or “politically relevant” (Hibbs 2006), component of NEE is attributable to policyrelated factors that differentiate among individuals in terms of their preferences over competing
policy goals and initiatives. An example here would be factors that differential individuals in
terms of the importance they give to controlling inflation rather than promoting job creation.
The systematic component of the measurement error in economic evaluations consists of
three broad factors. First, economic evaluations are shaped by partisanship. Duch and Palmer
(2002) have demonstrated that NEE is to a large extent shaped by partisan pre-dispositions – this
is the case in both the American and European contexts. In addition to political partisanship,
respondents may rely on personal experiences and regional economic circumstances to formulate
an evaluation of the national economy. Citizens who infer national conditions from personal and
local experiences are effectively evaluating the economy in a subjective rather than objective
manner. Similarly, NEE may vary across individuals due to differences in levels of information
14
and sophistication about government policy and economic outcomes. Third, social class
differences may systematically shape economic evaluations. Individuals in different
socioeconomic circumstances might view the same economy in a very different light (MacKuen
and Mouw 1995). Similarly, citizens of different sexes and races may perceive the economy
differently due to biases in their general attitudes toward the economic and political systems (on
gender and economic voting, see Welch and Hibbing 1992).
As posited earlier, measurement error in economic evaluations is likely to represent a
problem for models of support for European Union and democratic satisfaction because the
systematic components of the measurement error are also correlated with these attitudinal
variables. We argued above that partisanship shapes economic evaluations. There is also strong
evidence suggesting that partisanship influences attitudes toward European Union. The classic
statement of this is Reif’s (1984) notion that European elections are "second order elections."
By second order effect we mean that support for European institutions is strongly correlated with
voter's evaluations of national incumbents. Also, contributing to the correlation between
partisanship and support for the EU is the fact that national parties having varying levels of
enthusiasm for the EU which tends to get reflected in the attitudes of their partisans.
We also suspect that government partisanship is correlated with democratic satisfaction.
Just as partisans are less likely to criticize the economic performance of incumbent governments
we expect that they will also have more positive views of the institutional status quo when their
party is in government. For this reason, we expect partisanship as a source of systematic
measurement error in NEE to inflate the statistical relationship between the (perceived) economy
and both measures of support for political institutions.
Additionally, we expect socioeconomic and demographic factors to contribute to the
inflation of estimated relationships in peripheral economic voting models. Again this
augmentation bias is due to the direct effects these sources of systematic measurement error have
on the dependent variables in peripheral models. For instance, the research of Gabel and Palmer
(1995; Gabel 1998a, 1998b) demonstrates that support for European integration varies with
individual-level differences in job skills and financial assets as reflected in the citizen’s
socioeconomic situation and life-cycle stage. These same variables also are correlated with the
systematic component of measurement error in economic evaluations ( X i S ) .
15
Statistical Hypotheses. Our theoretical arguments can be summarized in three statistical
hypotheses. First, we argue that measures of national economic evaluations contain systematic
measurement error. We demonstrate this by specifying models of national economic evaluation
in four European countries. Our expectations regarding the model of national economic
evaluations are summarized in Table 1. We expect two sets of variables to affect variation in
national economic evaluations: a set of variables that reflect “Policy Related Variation” and a set
of variables capturing systematic measurement error. The “Policy Related Variation” represents
differences in economic evaluations that are grounded in self-interested differences in opinion
regarding measures of economic performance. Individuals who value reducing inflation more
than maintaining low unemployment (e.g., retired respondents on fixed incomes) might
emphasize price stability more than job creation when evaluating the national economy.
Table 1: Assumptions Dictating the Measurement Error Specification for National Economic Evaluations
Sources of Policy-Related Variation
Unemployment Concern
Self-Employed
Reduced Government Role
Farmer
Family Income
Retired
Manual Worker
Distinct Sources of Systematic Measurement Error
Government Partisanship
Age
Retrospective Personal Financial Situation
Sex
Personally Unemployed
Race
Education
Region
Cognitive Mobilization
16
On the other hand, “Distinct Sources of Systematic Measurement Error” consist of
variations in economic evaluations that are unrelated to policy concerns. For example,
evaluations of the national economy that are shaped by partisan attitudes are certainly not
grounded in any “objective” assessment of economic performance, nor do they reflect a policyrelated weighting of different economic outcomes (e.g., a preference for job growth rather than
price stability). The variables listed in the bottom half of Table 1 constitute systematic
measurement error because they contribute to variation in national economic evaluations that
cannot plausibly be associated with preferences for particular economic policy goals or
initiatives. Older respondents might be unhappy because they have not benefited from robust
economic growth but this in itself does not reflect an obvious policy orientation. These older
respondents are unhappy because they are net losers but this does not reflect a differential
weighting of economic policy goals.
A second hypothesis simply posits that in traditional specifications of conventional
economic voting models (with incumbent government support as the dependent variable) and of
“theory drift” models (with democratic satisfaction and support for the EU) national economic
evaluations will be statistically significant. In the former case, the significant statistical
relationship reflects the existence of a causal relationship between the economy as a policy
outcome and political support for the incumbent government. But in the latter case, we contend
that the significant statistical relationship is an artifact of systematic measurement error whose
sources produce a spurious relationship due to their direct effects on support for political
institutions.
Our third (and critical) hypothesis here is that controlling for the systematic measurement
error in national economic evaluations should produce two distinct outcomes. First, in the case
of the incumbent vote models (i.e., the conventional economic voting models), we expect to
observe no change or an improvement in the estimated effect of national economic evaluations
on vote choice. Second, in the case of the “peripheral” economic voting models—democratic
satisfaction and EU support—we expect to find that controlling for measurement error in NEE
significantly weakens, if not eliminates, the evidence of an economic voting relationship.
17
Results
As Table 1 indicates, we hypothesize that there is heterogeneity in national economic
evaluations – some of it policy related and some reflects measurement error. Table 2 presents
estimated ordered probit equations of national economic evaluations in each of the four
European countries. Three variables that we suspect are the primary source of systematic
measurement error in NEE are highly significant across the four samples: government
partisanship (i.e., favoring a party in the governing coalition), retrospective personal financial
situation, and whether the respondent was unemployed. With the exception of partisanship in
West Germany and the unemployed variable in Italy, these variables have statistically significant
effects in all four equations. The other hypothesized sources of systematic measurement error
are less uniformly significant in the model. The United Kingdom might be the exception since
education, sex and race all achieve statistical significance. Overall, there is clear evidence of
systematic measurement error in national economic evaluations.
In addition to the systematic measurement error, heterogeneity in NEE is also driven by
policy-related factors. The two variables making the most significant contribution in this regard
are unemployment concerns (significant in all but France) and support for a reduced government
role in the economy (significant in France and Italy). Once again the UK seems to exhibit the
most policy-related heterogeneity in retrospective evaluations of the economy. But on balance,
there are reasonable levels of policy-related heterogeneity in all four of these NEE equations.
The results in Table 2 confirm that national economic evaluations are shaped by both policyrelated factors and systematic measurement error.
18
Table 2: Ordered Probit Models of Retrospective National Economic Evaluations, EuroBarometer 21 (1984)
France
Coeff
T-stat
W.Germany
Coeff
T-stat
Italy
UK
Coeff
T-stat
Coeff
T-stat
Government Partisanship
.211**
6.35
.066
1.68
.162**
5.36
.288**
10.51
Retrospective PFS
.318**
8.09
.526**
11.14
.431**
9.83
.340**
10.49
Personally Unemployed
-.61*
-2.36
-.45*
-2.53
.47
1.63
.49*
2.35
Unemployment Concern
.027
.62
-.317**
-7.69
-.158**
-3.85
-.223**
-5.92
-.303**
-7.07
.031
.77
-.128**
-3.29
-.029
-.76
Family Income
.010
.54
.003
.21
-.021
-1.39
.0021
.10
Manual Worker
-.32
-1.61
-.29*
-2.00
.11
.54
.40*
2.40
Self-Employed
-.07
-.29
-.32*
-2.12
.17
.84
.45*
2.43
Farmer
-.27
-1.15
.32
1.12
-.10
-.35
.26
.57
Retired
-.10
-.47
-.18
-1.14
.33
1.69
.39*
2.26
Education
-.026
-1.55
.013
.92
.032*
2.20
.067**
3.39
Cognitive Mobilization
-.036
-.89
.043
1.04
.050
1.20
-.003
-.07
Age
-.0019
-.65
-.0004
-.16
-.0025
-1.00
.0049
1.94
Female
-.10
-1.36
-.18*
-2.33
-.05
-.72
-.21**
-2.86
White
-.28
-1.41
-.18
-1.32
.28
1.43
.60**
3.42
Region 2
-.04
-.33
-.04
-.27
-.08
-.75
.05
.40
Region 3
-.07
-.42
.28
1.84
-.07
-.60
.12
1.25
Region 4
-.35**
-2.81
.29
1.78
-.12
-1.17
-.09
-.63
Region 5
-.08
-.65
.33*
2.10
-.27*
-2.22
-.01
-.06
Region 6
-.20
-1.26
.41**
2.62
~~~
-.16
-1.29
Region 7
-.24
-1.82
~~~
~~~
~~~
Constant
.80**
3.16
.71**
2.59
.02
.07
-.02
-.09
1.17**
21.93
1.21**
15.30
1.15**
22.79
.93**
17.64
2.54**
29.17
2.72**
29.69
1.88**
30.41
1.83**
27.54
4.56**
29.46
3.38**
26.51
3.57**
32.02
Reduced Government Role
µ1
µ2
µ3
χ2
~~~
304.9**
284.7**
184.6**
432.3**
% Predicted Correctly
49.1
53.2
42.0
44.7
% Error Reduction
19.2
10.6
6.6
22.5
N
969
955
1019
1009
statistic for entire model
Note: Dependent variable is retrospective assessment of the general economy over the last 12 months. See appendix
for further details. **p<.01; *p<.05
19
Our second hypothesis is that national economic evaluations prove highly significant in
all three models – support for the incumbent, democratic satisfaction, and support for
membership in the European Union—when there are no controls for systematic measurement
error in the equation. Table 3 reports the results for binomial probit models of incumbent
support (where the dependent variable indicates whether the respondent would vote for a party in
the governing coalition). Clearly other factors in this model contribute to the explanation of vote
choice, but nevertheless retrospective evaluations of the economy are consistently significant in
all four equations. As we would expect, positive retrospective evaluations of the economy
generate a greater likelihood that respondents will vote for an incumbent party. In a similar
fashion the ordered probit results in Tables 4 and 5 are consistent with other estimations of
models derived from “theory drift” in that they show statistically significant and positive
coefficients for the NEE variable (with one exception out of eight). It is results like those
reported in Table 4 and 5 that have generated widespread consensus regarding the empirical
validity of these “peripheral” economic voting models.
20
Table 3: Binomial Probit Models of Incumbent Support, EuroBarometer 21 (1984)
France
W.Germany
Italy
UK
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Retrospective Eval. of Economy
.185**
2.90
.371**
5.79
.232**
5.75
.488***
9.82
Left-Right Ideology
-.601**
-15.03
.415**
12.76
.040
1.86
.354**
10.94
-.044
-1.07
.153**
3.16
.205**
4.81
.028
.81
.25
.90
.02
.09
.10
.34
-.42
-1.56
Family Income
-.026
-1.02
.017
.96
.024
1.40
.064**
2.66
Manual Worker
.12
.95
-.06
-.52
-.065
-.64
-.39**
-3.44
Education
-.026
-1.13
.026
1.27
-.012
-.71
.009
.36
Age
.0035
1.04
.0101**
3.37
.0075**
2.77
-.0026
-.81
Female
.28**
2.69
.06
.66
.13
1.60
.13
1.28
Region 2
-.21
-1.19
-.46*
-2.11
-.06
-.53
.15
1.05
Region 3
.16
.69
-.33
-1.62
-.03
-.25
.14
1.08
Region 4
.04
.22
-.39
-1.81
.14
1.19
.12
.60
Region 5
-.14
-.78
-.19
-.85
.01
.09
-.35*
-2.17
Region 6
.21
1.01
-.23
-1.09
~~~
-.05
-.27
Region 7
-.13
-.72
~~~
~~~
~~~
Constant
1.64**
5.06
-3.61**
Strength of Religious Faith
Personally Unemployed
χ2
-10.64
-1.70**
-6.97
-3.37**
-10.61
471.7**
392.8**
107.9**
417.1**
% Predicted Correctly
83.4
78.6
62.2
78.8
% Error Reduction
49.5
47.3
13.0
36.7
N
1009
992
1060
1042
statistic for entire model
Note: Dependent variable indicates whether respondents would vote for a party in governing coalition if a general
election were held tomorrow. See appendix for further details. **p<.01; *p<.05
21
Table 4: Ordered Probit Models of Democratic Satisfaction, EuroBarometer 21 (1984)
France
W.Germany
Italy
UK
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Retrospective Eval. of Economy
.399**
8.29
.348**
7.60
.242**
6.87
.286**
8.09
Left-Right Ideology
-.129**
-6.01
.125**
6.15
.033
1.86
.070**
3.02
Extremity of Left-Right
Ideology
-.085**
-2.84
-.054
-1.75
-.135**
-5.13
.006
.18
Family Income
.046**
2.69
.026
1.75
.034*
2.41
.025
1.43
.066
1.78
-.069
-1.62
.048
1.13
-.075
-1.89
Education
.047**
3.08
-.005
-.35
-.039**
-2.74
.024
1.29
Age
.0084**
3.75
.0071**
3.07
-.0018
-.79
.0022
.97
Female
-.08
-1.03
-.01
-.10
.05
.69
.060
.83
Constant
.45*
2.23
.48*
2.26
.11
.58
.37*
1.96
1.19**
21.52
1.21**
13.81
1.36**
26.07
1.05**
18.02
2.90**
30.79
3.29**
31.69
2.77**
25.34
2.70**
34.94
Cognitive Mobilization
µ1
µ2
χ2
218.1**
139.9**
103.9**
130.9**
% Predicted Correctly
47.7
65.5
48.7
52.3
% Error Reduction
15.1
2.8
3.0
0.8
N
935
922
1017
984
statistic for entire model
Note: Dependent variable is 4-category measure of respondents’ level of satisfaction with how democracy works in
their country. See appendix for further details. **p<.01; *p<.05
22
Table 5: Ordered Probit Models of Support for EC Membership, EuroBarometer 21 (1984)
France
W.Germany
Italy
UK
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
.217**
4.14
.094
1.87
.133**
3.04
.262**
7.12
Left-Right Ideology
.035
1.51
-.022
-.97
.022
1.04
.048*
2.20
Extremity of Left-Right
Ideology
-.046
-1.36
.055
1.59
-.080**
-2.66
-.037
-1.21
Family Income
.045*
2.24
-.004
-.24
.049**
2.87
.035*
2.03
Cognitive Mobilization
.091*
2.05
.026
.52
.049
.99
.053
1.32
Education
.078**
4.13
.049**
3.02
.014
.83
.086**
4.59
Age
.0050
1.89
-.0011
-.40
-.0009
-.31
.0082**
3.52
Female
-.33**
-3.90
.07
.78
-.10
-1.15
.04
.54
Constant
.82**
3.54
1.28**
5.45
1.39**
5.93
-.97**
-5.21
µ1
1.31**
18.03
1.34**
19.24
1.14**
14.89
.91**
20.58
Retrospective Eval. of Economy
χ2
77.9**
18.5*
40.0**
133.2**
% Predicted Correctly
67.7
59.2
75.0
47.0
% Error Reduction
4.1
0.3
0
18.7
N
938
887
985
986
statistic for entire model
Note: Dependent variable is a 3-category measure of respondents’ evaluation of whether their country’s
membership in the European Community is a good thing. See appendix for further details. **p<.01; *p<.05
23
As discussed above, we contend that the economic voting models of democratic
satisfaction and EU support in Tables 4 and 5 are fundamentally flawed due to their failure to
account for measurement error. More specifically, we hypothesize that the estimated economic
voting relationships in these models are spurious—attributable to the systematic measurement
error in retrospective evaluations of the national economy rather than the existence of a causal
relationship between policy outcomes and support for political institutions. Hence, an estimation
method that properly models this measurement error will reduce the strength of the economic
voting relationships in Tables 4 and 5. This is the second part of the third statistical hypothesis
specified above.
Note that in the traditional “errors in variables” model, controlling for the measurement
error in a regressor eliminates the attenuation of that regressor’s estimated effect. The traditional
setup, though, assumes that the measurement error is random. As discussed above and shown in
Table 2, as well as elsewhere (Duch and Palmer 2002), retrospective evaluations of the national
economy contain systematic measurement error attributable to government partisanship, personal
experiences and demographic characteristics. We contend here that the sources of this
systematic measurement error have independent effects on democratic satisfaction and EU
support. Hence, economic voting models that do not control for this systematic measurement
error, like those in Tables 4 and 5, will potentially produce augmented estimates of the
relationship between economic evaluations and support for political institutions.
In order to test this contention, we re-estimated the models in Tables 3-5 using a method
developed by Palmer (1999). In his analysis of pocketbook economic voting, Palmer (1999)
adapted a method proposed by Rivers and Vuong (1988) for (recursive) simultaneous probit
models. Their estimator is a two-stage conditional maximum likelihood (2SCML) estimator that
applies directly to the case of random measurement error. Palmer adapted the Rivers-Vuong
method to the case of systematic measurement error. The adaptation involves the construction of
an augmented residual that includes both systematic and random components of measurement
error.
More specifically, the adapted Rivers-Vuong method consists of two steps (discussed in
the specific context of the present analysis). The first step is to regress national economic
evaluations on a set of exogenous explanatory variables in order to model the measurement
24
error. This set of variables should capture “meaningful” or policy-oriented variation as well as
theorized sources of measurement error in national economic evaluations. Table 1 reviews our
assumptions about the nature of the heterogeneity in national economic evaluations, which
serves as the theoretical basis for our specification of the systematic measurement error. The
first-stage least squares (LS) regressions used to model the measurement error are presented in
Table A5 in the Appendix.
Under the assumption of random measurement error, the estimate of the measurement
error is simply the first-stage LS residuals. But in the present context, we constructed an
augmented residual that incorporates the sources of systematic measurement error. More
( )
specifically, the augmented residual Xˆ i is derived by subtracting the policy-related portion of
the first-stage LS fitted value (i.e., the portion explained by the variables listed in the top half of
Table 1) from the national economic evaluation.
Xˆ i = X i − X i O
(1.3)
Hence, the augmented residual combines random variation ( ε i ) and “explained” variation
attributable to theorized sources of systematic measurement error ( X i S ) .
The second step in the adapted Rivers-Vuong method is to include the augmented
( )
residual Xˆ i , as an estimate of the measurement error, in the (binomial or ordered) probit model
along with national economic evaluations.
(1.4)
Yi = β 0 + β1X i + β 2 Xˆ i + φ1Z i
Essentially, the augmented residual obtained from the first-stage regression controls for the
effect(s) of the measurement error. If the sources of systematic measurement error do not have
direct effects on the dependent variable, then the augmented residual simply controls for the
“noise” in the measure of national economic evaluations, thereby eliminating the attenuation
bias. However, if the sources of systematic measurement error directly influence the dependent
variable (as theorized here for democratic satisfaction and EU support), then the augmented
residual also captures these direct effects, potentially eliminating a spurious relationship.
Tables 6A-6D present 2SCML models of incumbent vote, democratic satisfaction and
EU
25
Table 6A: Comparison of Economic Voting Models that Account for Measurement Error, France
Incumbent Vote
Demo. Satisfaction
EU Support
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Retrospective Evaluation of Economy
.675*
1.97
.708**
3.46
.242
.98
Augmented Residual
-.501
-1.46
-.318
-1.55
-.026
-.11
Left-Right Ideology
-.561**
-11.67
-.105**
-3.99
.037
1.23
Extremity of Ideology
~~~
-.086**
-2.85
-.046
-1.36
Cognitive Mobilization
~~~
.061
1.64
.091*
2.00
Family Income
-.031
-1.23
.043*
2.51
.045*
2.22
Education
-.030
-1.29
.043**
2.77
.077**
4.01
Manual Worker
.21
1.50
~~~
~~~
Personally Unemployed
.22
.79
~~~
~~~
Strength of Religious Faith
-.041
-1.01
~~~
~~~
Age
.0039
1.15
.0084**
3.71
.0050
1.88
Female
.27**
2.59
-.08
-1.11
-.33**
-3.90
Region 2
-.22
-1.26
~~~
~~~
Region 3
.16
.68
~~~
~~~
Region 4
.03
.20
~~~
~~~
Region 5
-.16
-.93
~~~
~~~
Region 6
.19
.90
~~~
~~~
Region 7
-.15
-.83
~~~
~~~
Constant
1.52**
4.56
.42*
2.06
.82**
3.52
~~~
1.19**
21.48
1.31**
18.03
~~~
2.91**
30.56
~~~
µ1
µ2
χ
2
473.8**
220.4**
77.9**
% Predicted Correctly
83.5
58.9
67.7
% Error Reduction
49.8
33.3
4.1
N
1009
935
938
statistic for entire model
26
Table 6B: Comparison of Economic Voting Models that Account for Measurement Error, West Germany
Incumbent Vote
Demo. Satisfaction
EU Support
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
.840**
2.60
.390
1.79
.451
1.84
Augmented Residual
-.483
-1.48
-.044
-.21
-.372
-1.47
Left-Right Ideology
.376**
9.06
.121**
4.30
-.054
-1.71
Retrospective Evaluation of Economy
Extremity of Ideology
~~~
-.054
-1.76
.053
1.54
Cognitive Mobilization
~~~
-.068
-1.60
.031
.61
Family Income
.015
.89
.026
1.74
-.0045
-.30
Education
.027
1.31
-.006
-.36
.048**
2.91
Manual Worker
.02
.18
~~~
~~~
Personally Unemployed
.01
.04
~~~
~~~
Strength of Religious Faith
.149**
3.08
~~~
~~~
Age
.0103**
3.41
.0071**
3.05
-.0013
-.51
.05
.56
-.01
-.11
.05
.62
Region 2
-.47*
-2.15
~~~
~~~
Region 3
-.33
-1.64
~~~
~~~
Region 4
-.41
-1.87
~~~
~~~
Region 5
-.20
-.92
~~~
~~~
Region 6
-.23
-1.09
~~~
~~~
Constant
-3.42**
-9.47
.50*
2.07
1.48**
5.47
~~~
1.22**
13.70
1.34**
19.19
~~~
3.29**
31.46
~~~
Female
µ1
µ2
χ2
395.0**
140.0**
20.7*
% Predicted Correctly
78.7
65.6
59.2
% Error Reduction
47.5
3.1
0.3
N
992
922
887
statistic for entire model
27
Table 6C: Comparison of Economic Voting Models that Account for Measurement Error, Italy
Incumbent Vote
Demo. Satisfaction
EU Support
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Retrospective Evaluation of Economy
-.182
-.66
.056
.25
.185
.67
Augmented Residual
.423
1.52
.190
.83
-.053
-.19
Left-Right Ideology
.047*
2.14
.036*
2.00
.021
.96
Extremity of Ideology
~~~
-.136**
-5.13
-.080**
-2.65
Cognitive Mobilization
~~~
.045
1.07
.049
.99
Family Income
.015
.82
.030*
2.04
.050**
2.80
Education
-.014
-.85
-.039**
-2.73
.014
.83
Manual Worker
-.09
-.90
~~~
~~~
Personally Unemployed
.04
.01
~~~
~~~
.20**
4.76
~~~
~~~
.0085**
3.05
-.0012
-.52
-.0010
-.35
Female
.13
1.52
.05
.66
-.10
-1.14
Region 2
-.06
-.50
~~~
~~~
Region 3
-.02
-.20
~~~
~~~
Region 4
.14
1.22
~~~
~~~
Region 5
.01
.09
~~~
~~~
Constant
-1.69**
-6.93
.10
.55
1.40**
5.92
~~~
1.36**
26.08
1.14**
14.83
~~~
2.77**
25.15
~~~
Strength of Religious Faith
Age
µ1
µ2
χ
2
110.2**
104.5**
40.0**
% Predicted Correctly
62.6
48.5
75.1
% Error Reduction
14.1
2.6
0
N
1060
1017
985
statistic for entire model
28
Table 6D: Comparison of Economic Voting Models that Account for Measurement Error, United Kingdom
Incumbent Vote
Demo. Satisfaction
EU Support
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
.875**
3.42
.277
1.59
-.103
-.57
Augmented Residual
-.392
-1.54
.008
.05
.369*
2.04
Left-Right Ideology
.329**
9.07
.070**
2.64
.075**
2.88
Retrospective Evaluation of Economy
Extremity of Ideology
~~~
.006
.18
-.040
-1.31
Cognitive Mobilization
~~~
-.075
-1.87
.045
1.11
Family Income
.065**
2.70
.025
1.43
.037*
2.16
.009
.36
.024
1.29
.083**
4.44
-.46**
-3.76
~~~
~~~
Personally Unemployed
-.33
-1.19
~~~
~~~
Strength of Religious Faith
.028
.83
~~~
~~~
-.0036
-1.10
.0022
.97
.0089**
3.75
Female
.14
1.42
.06
.81
.02
.31
Region 2
.15
1.00
~~~
~~~
Region 3
.15
1.15
~~~
~~~
Region 4
.11
.58
~~~
~~~
Region 5
-.34*
-2.07
~~~
~~~
Region 6
-.04
-.25
~~~
~~~
Constant
-3.28**
-10.14
.37
1.93
-1.05**
-5.42
~~~
1.05**
18.01
.92**
20.56
~~~
2.70**
34.89
~~~
Education
Manual Worker
Age
µ1
µ2
χ2
419.5**
130.9**
137.4**
% Predicted Correctly
79.5
52.3
47.1
% Error Reduction
38.7
0.8
18.8
N
1042
984
986
statistic for entire model
29
support for France, West Germany, Italy and the United Kingdom. Augmented Residual controls
for the measurement error in Retrospective Evaluation of Economy. According to our “theory
drift” argument, the 2SCML models should provide weaker empirical support for peripheral
economic voting models of democratic satisfaction and EU support than do the “naïve”
specifications in Tables 4 and 5. In other words, the inclusion of Augmented Residual should
account for the spurious relationship due to systematic measurement error. In contrast, the
2SCML models of incumbent vote should produce stronger estimates of the economic voting
relationship since they control for the confounding effect of measurement error in retrospective
evaluations of the national economy. By controlling for measurement error, the 2SCML models
produce more precise or “cleaner” estimates of the relationship between politically relevant
macro-economic outcomes and political support for the incumbent government.
A comparison of Table 3 with the incumbent vote models in Tables 6A-6D reveals that
controlling for measurement error strengthens the economic voting relationship considerably for
France, West Germany, and the United Kingdom. Figure 1 illustrates the differences in the
estimated economic voting relationship for these three countries. The “no measurement error”
lines plot the estimated relationships from Table 3, while the “measurement error” lines plot
those from Tables 6A, 6B and 6D. Clearly, proper modeling of the measurement error increases
the marginal effect of retrospective evaluations on the probability of an incumbent vote.
30
Figure 1: Comparison of Estimated Effects of Retrospective Economic Evaluations on Incumbent Vote
1
Predicted Probability
0.8
0.6
0.4
France, no measurement error
France, measurement error
0.2
W.Germany, no measurement error
W.Germany, measurement error
UK, no measurement error
UK, measurement error
0
0
1
2
3
4
Retrospective Evaluation of Economy
In contrast, the 2SCML models of democratic satisfaction produce weaker evidence of an
economic voting relationship for West Germany, Italy, and the United Kingdom (though the
economic effect for France is considerably stronger). While Table 4 indicates that retrospective
evaluations of the national economy significantly influence the likelihood of an incumbent vote
at better than the 1% level, the estimated sociotropic effect in these countries becomes
insignificant at the 5% level once the model controls for systematic measurement error. This
finding supports our claim that measurement error produces the spurious relationships reported
in Table 4 and hence undermines the validity of peripheral economic voting models of
democratic satisfaction.
Similarly, the 2SCML models of EU support produce weaker evidence of an economic
voting relationship for France, Italy, and the United Kingdom. Again, the “naïve” model
specifications in Table 5 for these countries produce estimated economic voting relationships
that are statistically significant at better than the 1% level. However, the models in Tables 6B,
6C and 6D that account for the systematic measurement error in retrospective evaluations of the
economy produce estimated effects that do not prove significant at the 5% level. In sum, the
31
contrast between core and peripheral economic voting models is greatest for West Germany and
the United Kingdom where the sociotropic effect is only significant in the incumbent vote
equation once the estimation method accounts for measurement error (see Tables 6B and 6D).
Furthermore, between these two countries, the United Kingdom produces results most consistent
with our arguments about theory drift and measurement error in that the “naïve” specifications
suggest that the economy matters in explaining democratic satisfaction and EU support among
the British public but it clearly does not in the estimates controlling for measurement error.
Discussion
In this essay, we address an empirical puzzle associated with economic voting models of
democratic satisfaction and public support for European Union. We contend that the application
of economic voting theory to explain public support for political institutions represents an
example of “theory shift” where scholars have presumed that a proven theory of incumbent
support also has relevance in “peripheral” contexts. Yet, traditional specifications of peripheral
economic voting models perform remarkably well despite their weak theoretical foundations.
This poses an empirical puzzle.
We characterize traditional specifications of peripheral economic voting models as
“naïve” since they fail to account for measurement error in evaluations of the economy. We then
demonstrate that the empirical puzzle is a product of poor model specification and systematic
measurement error. More specifically, our analysis shows that controlling for the systematic
measurement error in national economic evaluations largely eliminates any evidence of a
relationship between the economy and public support for democratic institutions at the national
and supranational levels.
This finding has several broader implications for economic voting research as well as
theory building in political science more generally. First, the individual-level evidence
presented here that systematic measurement error can augment as well as attenuate estimates of
economic voting relationships complements recent aggregate-level research. This is consistent
with our earlier findings that measurement error due to partisanship, personal financial
experiences, and information can systematically bias relationships between incumbent popularity
and aggregate series of citizens’ economic evaluations (Duch, Palmer and Anderson 2000).
32
Second, our analysis calls into question the validity of all peripheral economic voting
models, not only those associated with public support for democratic institutions. Essentially,
any “economic voting” argument that relies on shifts in the public’s economic mood or on ad
hoc emotional responses to short-run economic circumstances is theoretically suspect. Rather
than blindly adapting the economic voting paradigm, studies of peripheral contexts should
develop original theories that incorporate the specific incentives faced by the relevant political
actors. If the economy does matter, it should motivate behavior in a sensible way specific to the
context being analyzed. Explanations of political behavior should rest on sound theoretical logic
rather than theory by association or analogy.
33
Appendix
The demographic variables employed in our analysis include a set of dummy variables
measuring occupation and employment status: farmer, manual, retired, unemployed, and selfemployed. Income is measured by income categories (or ranges) that vary between 10 and 12
depending upon the specific European country. Education is measured by the age at which
respondents completed their studies (adjusted for those respondents who indicated that they were
still studying). Descriptive statistics for all of the explanatory variables are reported in Table
A2.
Regional dummy variables. We speculate that perceptions of the national economy vary
regionally given variations in regional economic circumstances. The analysis includes a series
of regional dummy variables based on the regional codes in the EuroBarometer study. These
codes are defined in Table A1.
We measure political sophistication with the same two items that comprise Inglehart’s
cognitive mobilization measure: “When you yourself hold a strong opinion, do you ever find
yourself persuading your friends, relatives or fellow workers to share your views? Is so, does
this happen often, from time to time, or rarely?” and “When you get together with your friends,
would you say you discuss political matters frequently, occasionally or never?”
Measures of economic circumstances. The central explanatory variable in our analysis
(and the dependent variable in the first-stage regression), retrospective evaluation of general
economy, is based on the following question: “How do you think the general economy has
changed over the last 12 months?” The response set is: got a lot worse, got a little worse, stayed
the same, got a little better, and got a lot better. Retrospective personal financial situation has
the same response set with the following question wording: “How does the financial situation of
your household compare with what it was 12 months ago?”
Measures of economic policy preferences. Perceptions of the economy may simply
reflect economic policy priorities held by individuals – for example individuals concerned with
unemployment issues may significantly discount improvements in economic fundamentals such
as growth rates (or possibly not even notice them) if there is no significant improvement in the
unemployment rates. Five items were included in EB21 that measure respondents’ economic
policy priorities:
34
•
A measure of concern for the issue of unemployment was constructed from
responses to the following question: “During the last year, have you (or some one
in your household) worried about losing a job or not finding a job?” Responses
were coded as follows: A lot (3); a little (2); not at all (1); and don’t know (1).
•
Respondents were also asked a series of agreed/disagreed questions that touched
on economic policy issues. We employed two of these: “Unemployment is
distressing”; and “The government should intervene less in the management of
the economy.” Responses were coded as follows: 1) disagree completely; 2)
disagree to some extent; 3) agree if anything; 4) broadly agree; 5) completely
agree. Missing values were set to the sample mean.
•
Respondents were asked the following question: “Here are some kinds of fears
which are sometimes expressed about the future (say 10 to 15 years) of the world
we live in. I would like you to tell me which of the following really concern you
or you worry about? Three of the “fears” concerned job issues: “foreign
workers”; “unemployment amongst the young”; and “loss of job”. A variable
measuring concern about job security was constructed by simply adding up how
many of these items were mentioned by the respondent.
•
Identification with either the Left or Right represents an expression of economic
policy preferences. To measure Left-Right placement, respondents were asked to
place themselves on a scale from 1 to 10 with 1 representing the Left and 10
representing the Right (“In political matters, people talk of ‘the Left’ and the
‘Right’. How would you place your views on this scale?”). Missing values are
set to the sample mean.
We included these five items in a factor analysis (for the entire EuroBarometer sample), which
generated two factor dimensions – a primary dimension tapping concern with unemployment
issues and job security, and a second dimension that suggesting a high degree of support for
reducing government management of the economy. These results are presented in Table A3.
Both factor scales are employed as proxies of economic policy preferences.
Measure of Partisanship. There is no direct measure of partisanship in the
EuroBarometer surveys. Nevertheless, the EB21 survey asks a series of questions asking
whether respondents would or would not consider voting for a particular partisan tendency. The
question wording is as follows: “I will now mention a few political movements. Please tell me
each time if it is possible or impossible for you to vote one day for a party which will correspond
to the description.” The response set was 1) possible or 2) impossible. We used these questions
to construct an incumbent partisanship measure that ranges from –2 for strong anti-incumbent
35
partisanship to +2 for strong pro-incumbent partisanship. This measure was coded as follows:
•
As Table A4 indicates, each of the political tendencies was associated with the
incumbent government (G) or with the opposition (O).
•
Respondents who indicated that they would vote for a political group associated
with the incumbent government but not for one associated with the opposition
were assigned a score of 2.
•
Respondents reporting that they would vote for a political group associated with
the government but also for any of the other non-government groupings were
assigned a score of 1.
•
Respondents indicating that they would vote for a grouping not associated with
the government but do not report that they would not vote for a grouping
associated with the government (these essentially are respondents who reported
“don’t know” to the question) were assigned a score of –1.
•
Respondents reporting that they would not vote for a grouping associated with the
government but would vote for a grouping associated with a non-government
grouping were assigned a score of –2.
Support for European Unification. Support for European unification is based on the
responses to the following question: “Generally speaking do you think that “France’s”
membership in the European Community is a 1) good think, 2) bad thing, 3) neither good nor
bad.”
Democratic Satisfaction. The measure of democratic satisfaction is based on responses to
the following question: “On the whole, are you very satisfied, fairly satisfied, not very satisfied
or not at all satisfied with the way democracy works in “France”?
36
Table A1: Region Codes
Country
Code
Region
% of Country’s Cases
France
Region 1
Northwest
16.5
(N=1009)
Region 2
Southwest
17.3
Region 3
North
6.7
Region 4
Paris Basin
17.9
Region 5
Paris Region
18.9
Region 6
East
7.6
Region 7
Southeast
15.0
Germany
Region 1
Schleswig-Holstein, Hambur
6.5
(N=992)
Region 2
Lower Saxony, Bremen, West Berlin
14.8
Region 3
Northrine-Westphalia
27.7
Region 4
Hesse, Rhineland-Palatinate, Saarland
16.5
Region 5
Baden-Wuerttemberg
15.2
Region 6
Bavaria
19.3
Italy
Region 1
Northwest
27.6
(N=1060)
Region 2
Northeast
18.5
Region 3
Central
19.1
Region 4
South
23.3
Region 5
Islands
11.5
United Kingdom
Region 1
North, Yorkshire-Humberside, Northwest
29.4
(N=1042)
Region 2
North Midlands, West Midlands
15.3
Region 3
East Anglia, Southeast
22.7
Region 4
Southwest
7.4
Region 5
Wales, Scotland
14.3
Region 6
Greater London
10.9
Note: Region 1 is used as the baseline for comparison in our econometric analysis (i.e., excluded from
specification). Respondents in Northern Ireland were excluded from the analysis.
37
Table A2: Descriptive Statistics
Explanatory Variables
Mean
S.D.
Min.
Max.
Government Partisanship
.24
1.27
-2
2
Retrospective PFS
1.73
.86
0
4
Personally Unemployed
.045
0
1
Unemployment Concern
0
1.00
-3.52
3.41
Reduced Government Role
0
1.00
-3.76
3.04
Family Income
5.81
2.63
0
11
Manual Worker
.274
0
1
Self-Employed
.122
0
1
Farmer
.028
0
1
Retired
.199
0
1
Education
2.36
2.56
0
9
Cognitive Mobilization
1.25
.94
0
3
Age
42.6
17.7
15
91
Female
.521
0
1
White
.274
0
1
Left-Right Ideology
4.38
1.90
0
9
Extremity of Ideology
1.41
1.28
0
4.5
Strength of Religious Faith
2.38
1.33
0
4
Note: Standard deviations are not reported for dummy variables.
38
Table A3: Factor Analysis of Economic Policy Preferences
Factor Loadings
Factors
Unemployment Concern
Reduced Government Role
Less Government
-.182
.797
Left-Right Ideology
-.486
.403
Unemployment Distress
.494
.524
Worry about Unemployment
.549
.031
Job Concern Count
.658
.098
Eigen Value
1.25
1.08
Variance Explained
24.9
21.7
Table A4: Coding of Government and Opposition Parties, April 1984
Extreme
Left
Extreme
Right
Nationalist
Socialist
Christian
Democrat
Communist
Fascist
Conservative
Ecologists
Liberal
Regionalist
France
O
O
O
G
O
G
O
O
O
O
O
UK
O
O
O
O
G
O
O
G
O
O
O
Germany
O
O
O
O
G
O
O
G
O
G
O
Italy
O
O
O
G
G
O
O
G
O
G
O
39
Table A5: OLS Models of Retrospective National Economic Evaluations
France
W.Germany
Italy
UK
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Coeff
T-stat
Government Partisanship
.144**
6.51
.044
1.69
.139**
4.97
.254**
11.38
Retrospective PFS
.216**
7.44
.356**
10.06
.361**
8.84
.281**
9.34
Personally Unemployed
-.39*
-2.20
-.31*
-2.29
.37
1.21
.37*
2.14
Unemployment Concern
.018
.59
-.210**
-7.37
-.133**
-3.52
-.188**
-5.53
-.199**
-6.83
.018
.66
-.107**
-3.00
-.025
-.81
Family Income
.008
.61
.002
.25
-.015
-1.10
.0007
.05
Manual Worker
-.22
-1.71
-.18
-1.90
.08
.40
.33*
2.31
Self-Employed
-.06
-.39
-.21
-1.93
.15
.69
.36*
2.26
Farmer
-.18
-1.20
.21
1.01
-.08
-.30
-.02
-.05
Retired
-.07
-.54
-.11
-1.05
.29
1.39
.32*
2.17
Education
-.018
-1.57
.008
.80
.030*
2.32
.059**
3.69
Cognitive Mobilization
-.016
-.59
.022
.78
.054
1.48
-.003
-.09
Age
-.0009
-.44
-.0002
-.11
-.0023
-1.07
.0042*
1.96
Female
-.07
-1.33
-.12*
-2.45
-.06
-1.01
-.17**
-2.77
White
-.20
-1.55
-.11
-1.21
.24
1.15
.49**
3.36
Region 2
-.02
-.23
-.04
-.33
-.06
-.61
.06
.69
Region 3
-.05
-.44
.18
1.71
-.04
-.47
.12
1.44
Region 4
-.23**
-2.74
.18
1.65
-.09
-.99
-.04
-.37
Region 5
-.05
-.61
.21
1.87
-.21
-1.90
-.02
-.21
Region 6
-.11
-1.04
.27*
2.43
~~~
-.13
-1.30
Region 7
-.15
-1.68
~~~
~~~
~~~
Constant
1.17**
6.62
1.13**
Reduced Government Role
6.26
.64*
2.47
.74**
3.72
16.2**
16.2**
9.9**
26.9**
R-squared
.264
.258
.159
.352
N
969
955
1019
1009
F-statistic for entire model
Note: Dependent variable is retrospective assessment of the general economy over the last 12 months. See appendix
for further details. These models were employed to construct the augmented residuals included in Tables 5-8 to
control for systematic measurement error. **p<.01; *p<.05
40
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